



"""simple docstring"""






import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification






def 			snake_case_    ( A_   :						Optional[int]		):


       '''simple docstring'''





       _lowerCamelCase		:							int            =					SwinConfig()
       _lowerCamelCase		:							Dict            =					swin_name.split('''_'''		)

       _lowerCamelCase		:							List[Any]            =					name_split[1]
       _lowerCamelCase		:							List[Any]            =					int(name_split[4]		)
       _lowerCamelCase		:							str            =					int(name_split[3][-1]		)

       if model_size == "tiny":
              _lowerCamelCase		:							Dict            =					96
              _lowerCamelCase		:							Tuple            =					(2, 2, 6, 2)
              _lowerCamelCase		:							List[Any]            =					(3, 6, 12, 24)
       elif model_size == "small":
              _lowerCamelCase		:							Union[str, Any]            =					96
              _lowerCamelCase		:							List[Any]            =					(2, 2, 18, 2)
              _lowerCamelCase		:							str            =					(3, 6, 12, 24)
       elif model_size == "base":
              _lowerCamelCase		:							int            =					1_28
              _lowerCamelCase		:							List[str]            =					(2, 2, 18, 2)
              _lowerCamelCase		:							Tuple            =					(4, 8, 16, 32)
       else:
              _lowerCamelCase		:							Optional[int]            =					1_92
              _lowerCamelCase		:							Any            =					(2, 2, 18, 2)
              _lowerCamelCase		:							Tuple            =					(6, 12, 24, 48)

       if "in22k" in swin_name:
              _lowerCamelCase		:							Optional[int]            =					2_18_41
       else:
              _lowerCamelCase		:							Optional[int]            =					10_00
              _lowerCamelCase		:							str            =					'''huggingface/label-files'''
              _lowerCamelCase		:							str            =					'''imagenet-1k-id2label.json'''
              _lowerCamelCase		:							Any            =					json.load(open(hf_hub_download(A_,							A_,							repo_type='''dataset'''		),							'''r'''		)		)
              _lowerCamelCase		:							Optional[int]            =					{int(A_		): v for k, v in idalabel.items()}
              _lowerCamelCase		:							List[str]            =					idalabel
              _lowerCamelCase		:							int            =					{v: k for k, v in idalabel.items()}

       _lowerCamelCase		:							List[Any]            =					img_size
       _lowerCamelCase		:							int            =					num_classes
       _lowerCamelCase		:							Optional[int]            =					embed_dim
       _lowerCamelCase		:							Optional[int]            =					depths
       _lowerCamelCase		:							Optional[Any]            =					num_heads
       _lowerCamelCase		:							Union[str, Any]            =					window_size

       return config






def 			snake_case_    ( A_   :						int		):


       '''simple docstring'''





       if "patch_embed.proj" in name:
              _lowerCamelCase		:							Optional[int]            =					name.replace('''patch_embed.proj''',							'''embeddings.patch_embeddings.projection'''		)
       if "patch_embed.norm" in name:
              _lowerCamelCase		:							Optional[int]            =					name.replace('''patch_embed.norm''',							'''embeddings.norm'''		)
       if "layers" in name:
              _lowerCamelCase		:							str            =					'''encoder.''' + name
       if "attn.proj" in name:
              _lowerCamelCase		:							List[Any]            =					name.replace('''attn.proj''',							'''attention.output.dense'''		)
       if "attn" in name:
              _lowerCamelCase		:							Dict            =					name.replace('''attn''',							'''attention.self'''		)
       if "norm1" in name:
              _lowerCamelCase		:							List[Any]            =					name.replace('''norm1''',							'''layernorm_before'''		)
       if "norm2" in name:
              _lowerCamelCase		:							List[Any]            =					name.replace('''norm2''',							'''layernorm_after'''		)
       if "mlp.fc1" in name:
              _lowerCamelCase		:							List[str]            =					name.replace('''mlp.fc1''',							'''intermediate.dense'''		)
       if "mlp.fc2" in name:
              _lowerCamelCase		:							Optional[int]            =					name.replace('''mlp.fc2''',							'''output.dense'''		)

       if name == "norm.weight":
              _lowerCamelCase		:							Any            =					'''layernorm.weight'''
       if name == "norm.bias":
              _lowerCamelCase		:							Union[str, Any]            =					'''layernorm.bias'''

       if "head" in name:
              _lowerCamelCase		:							Dict            =					name.replace('''head''',							'''classifier'''		)
       else:
              _lowerCamelCase		:							List[Any]            =					'''swin.''' + name

       return name






def 			snake_case_    ( A_   :						List[str],							A_   :						int		):


       '''simple docstring'''





       for key in orig_state_dict.copy().keys():
              _lowerCamelCase		:							int            =					orig_state_dict.pop(A_		)

              if "mask" in key:
                     continue
              elif "qkv" in key:
                     _lowerCamelCase		:							Optional[int]            =					key.split('''.'''		)
                     _lowerCamelCase		:							Dict            =					int(key_split[1]		)
                     _lowerCamelCase		:							Dict            =					int(key_split[3]		)
                     _lowerCamelCase		:							List[Any]            =					model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                     if "weight" in key:
                            _lowerCamelCase		:							Optional[Any]            =					val[:dim, :]
                            _lowerCamelCase		:							int            =					val[
                                dim : dim * 2, :
                            ]
                            _lowerCamelCase		:							Optional[Any]            =					val[-dim:, :]
                     else:
                            _lowerCamelCase		:							Optional[int]            =					val[
                                :dim
                            ]
                            _lowerCamelCase		:							int            =					val[
                                dim : dim * 2
                            ]
                            _lowerCamelCase		:							Union[str, Any]            =					val[
                                -dim:
                            ]
              else:
                     _lowerCamelCase		:							List[Any]            =					val

       return orig_state_dict






def 			snake_case_    ( A_   :						Any,							A_   :						Tuple		):


       '''simple docstring'''





       _lowerCamelCase		:							Union[str, Any]            =					timm.create_model(A_,							pretrained=A_		)
       timm_model.eval()

       _lowerCamelCase		:							Union[str, Any]            =					get_swin_config(A_		)
       _lowerCamelCase		:							Union[str, Any]            =					SwinForImageClassification(A_		)
       model.eval()

       _lowerCamelCase		:							Tuple            =					convert_state_dict(timm_model.state_dict(),							A_		)
       model.load_state_dict(A_		)

       _lowerCamelCase		:							Dict            =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''

       _lowerCamelCase		:							Optional[int]            =					AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_''',							'''-'''		)		)		)
       _lowerCamelCase		:							str            =					Image.open(requests.get(A_,							stream=A_		).raw		)
       _lowerCamelCase		:							Tuple            =					image_processor(images=A_,							return_tensors='''pt'''		)

       _lowerCamelCase		:							Dict            =					timm_model(inputs['''pixel_values''']		)
       _lowerCamelCase		:							Union[str, Any]            =					model(**A_		).logits

       assert torch.allclose(A_,							A_,							atol=1E-3		)

       print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''		)
       model.save_pretrained(A_		)

       print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
       image_processor.save_pretrained(A_		)


if __name__ == "__main__":
   lowerCAmelCase__              =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--swin_name''',
       default='''swin_tiny_patch4_window7_224''',
       type=str,
       help='''Name of the Swin timm model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
   )

   lowerCAmelCase__              =							parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					int      =       {
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'deit'
    def __init__(			self     ,   __snake_case=7_6_8     ,   __snake_case=1_2     ,   __snake_case=1_2     ,   __snake_case=3_0_7_2     ,   __snake_case="gelu"     ,   __snake_case=0.0     ,   __snake_case=0.0     ,   __snake_case=0.02     ,   __snake_case=1E-12     ,   __snake_case=2_2_4     ,   __snake_case=1_6     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=1_6     ,   **__snake_case     ,   ):
         super().__init__(**__snake_case       )

         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							initializer_range
         snake_case											=							layer_norm_eps
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							qkv_bias
         snake_case											=							encoder_stride






class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						version.parse('1.11'      )
    @property
    def 						a_     (			self       ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )




    @property
    def 						a_     (			self       ):
         return 1E-4




